Effects of Database Size on Rule System Performance: Five Case Studies
Summary: Five real-case studies show database size driving inferencing performance in rule-based systems, with state size a key bottleneck. Comparing TREAT and LEAPS lazy matching, results indicate lazy matching reduces state and improves performance on large datasets. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. David A. Brant
- 2. Timothy Grose
- 3. Bernie Lofaso
- 4. Daniel P. Miranker
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,392 | Index Support for Rule Activation | 1993 | SIGMOD | 5.0841639e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,075 | Compiling complex database transition triggers | 1989 | SIGMOD | 9.6076188e-05 |
| 2,557 | Making Deductive Database a Practical Technology: a step forward | 1990 | SIGMOD | 8.5462449e-05 |
| 5,831 | MOBY: An Architecture for Distributed Expert Database Systems | 1987 | VLDB | 5.3113542e-05 |
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